The invention described herein generally relates to a patient monitor, and in particular, a system, method and software program product for analyzing video frames of a patient and determining from motion within the frame if the patient is at risk of a fall.
Fall reduction has become a major focus of all healthcare facilities, including those catering to permanent residents. Healthcare facilities invest a huge amount of their resources in falls management programs and assessing the risk of falls in a particular patient class, location, and care state, along with the risk factors associated with significant injuries. Round the clock patient monitoring by a staff nurse is expensive, therefore, healthcare facilities have investigated alternatives in order to reduce the monitoring staff, while increasing patient safety. Healthcare facilities rely on patient monitoring to supplement interventions and reduce the instances of patient falls.
Many patient rooms now contain video surveillance equipment for monitoring and recording activity in a patient's room. Typically, these video systems compare one video frame with a preceding frame for changes in the video frames that exceed a certain threshold level. More advanced systems identify particular zones within the patient room that are associated with a potential hazard for the patient. Then, sequential video frames are evaluated for changes in those zones. Various systems and methods for patient video monitoring have been disclosed in commonly owned U.S. Patent Application Nos. 2009/0278934 entitled System and Method for Predicting Patient Falls, 2010/0134609 entitled System and Method for Documenting Patient Procedures, and 2012/0026308 entitled System and Method for Using a Video Monitoring System to Prevent and Manage Decubitus Ulcers in Patients, each of which is incorporated herein by reference in its entirety.
Such automated systems may be susceptible to false-alarms, which can burden a staff of healthcare professionals with unnecessary interventions. For example, a false-alarm can be triggered by patient activity that is not indeed indicative of an increased risk of a patient fall. A false-alarm can also be triggered by the activity of a visitor (e.g., healthcare professional, family of patient) around the patient. While the aforementioned systems is capable of detecting potential falls using image processing techniques, there currently exists opportunities to improve the accuracy of such systems to reduce the number of false positives detected by such systems.
The inventions disclosed herein improve upon the previously discussed systems for identifying and analyzing video frames to detect potential falls by employing supervised learning techniques to improve the accuracy of fall detection given a plurality of video frames. Specifically, the present disclosure discusses techniques for analyzing a set of key features that indicate when a fall is about to occur. By identifying key features, the present disclosure may utilize a number of supervised learning approaches to more accurately predict the fall risk of future video frames.
Embodiments of invention disclosed herein provide numerous advantages over existing techniques of analyzing image frame data to detect falls. As an initial improvement, the use of multiple image frames corrects training data to remove noise appearing due to changes in lighting. During testing, the use of a classifier, versus more simplistic comparison, yield at an accuracy level of approximately 92%. Thus, the embodiments of the disclosed invention offer significantly improved performance over existing techniques in standard conditions, while maintaining a consistent increase in performance in sub-optimal conditions (e.g., dim or no lighting).